Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits

Synthesis: This exploratory study investigates how undergraduates use LLMs to debug malfunctioning analog circuits under exam conditions, identifying both promising collaborative patterns and critical limitations. Through thematic analysis of student chat logs, the authors find that off-the-shelf LLMs offer considerable domain knowledge and sensible debugging suggestions, yet struggle with 2D/3D image-based reasoning and display unjustified confidence. Students, in turn, show deficits in fundamental concepts and critical thinking during AI-assisted debugging.

Study Design

Context: Undergraduate students debugging predetermined buggy analog circuits on breadboards and PCBs

Conditions: Exam setting with time pressure

Data: Voluntarily shared chat logs from student-LLM conversations

Analysis: Thematic analysis of interaction patterns

Key Findings

What Worked

LLMs provided sensible debugging suggestions grounded in domain knowledge

Students developed multimodal usage patterns — combining text descriptions, code snippets, and circuit descriptions

AI-assisted debugging showed potential for scaffolding learning by guiding students through systematic troubleshooting

What Didn't Work

Limitation	Description
2D/3D image reasoning	LLMs failed to interpret circuit board images and diagrams
Unjustified confidence	LLMs presented incorrect diagnoses with high confidence
Student fundamental gaps	Students lacked core concepts needed to evaluate AI suggestions
Critical thinking deficits	Students over-relied on AI outputs without verification

Implications for AI in Engineering Education

This study contributes to understanding AI's role in engineering education by revealing a dual challenge:

1. Tool limitations: Current LLMs are not yet reliable for tasks requiring spatial reasoning about physical circuits

2. Pedagogical challenge: Simply providing AI access does not replace the need for strong fundamental knowledge — students must develop the critical thinking skills to evaluate AI outputs

The findings support a Scaffolding approach where AI tools complement rather than replace instructor-guided learning in hands-on engineering contexts.

Connected Concepts

LLM

Human AI Collaboration

Computational Thinking

CS Education

Scaffolding

Connected Articles

Generativism Learning Theory — Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence

Trio Ethnography LLM Programming Education — Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education

LLM Intervention Design CS Review — A review of intervention designs of LLM Integration in Undergraduate Computer Science Education

Student Misconceptions Conditionals Loops Taxonomy — How Students (Mis)understand Conditionals and Loops -- A Taxonomy

LLM Design Problems Hot PjBL — LLM-Generated Design Problems for Assessing Higher-Order Thinking in Project-Based Learning

Spritz AI Disciplinary Mediation Student Teams 2026 — Exploring AI-Supported Disciplinary Mediation in Student Project Teams' Text-Based Communication

Citation

Hu, J., & Ash, A. (2026). Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits. arXiv:2608.02955v1.